The Problem: Your Data Doesn't Talk to Each Other
A mid-sized e-commerce company today runs five, ten, or twenty systems. Shopware or Shopify for the online store. SAP or another ERP for inventory and accounting. Salesforce or HubSpot for customer management. Google Ads and Meta Ads for marketing. Plus fulfillment systems, marketplaces like Amazon and eBay, an email tool, and maybe a BI dashboard that was set up three years ago.
Each of these systems collects data. But each speaks its own language. A product has a different ID in Shopware than in SAP. A customer has a different name in the CRM than in accounting. Marketplace orders show up in the ERP days later.
The result: data silos. Islands of information that exist in isolation and never complement each other.
According to a Handelsblatt analysis, 76% of IT leaders say data silos are blocking the digital transformation of their companies. This is not an edge case. It is the norm.
Why It's Getting Worse Right Now
Three developments are amplifying the silo problem simultaneously:
1. More channels, more systems, more fragments. Omnichannel is no longer optional — it's a requirement. Anyone selling on Amazon, in their own shop, and through social commerce automatically has three data pools that don't match up. Every new channel brings a new system — and a new silo.
The consequences are tangible: products are shown as available when they've been out of stock for days. Prices differ between channels because changes don't sync in time. Customers order twice because order status isn't visible across channels.
2. AI needs connected data — and doesn't get it. AI projects don't fail because of technology. They fail because of the data they're supposed to run on. According to a Shopware merchant survey, 89% of B2B companies say data quality is the biggest obstacle when implementing AI. Not the algorithm. Not the budget. The data.
The trade publication etailment puts it bluntly: "Data foundation first, then automation." Skip this step and you automate chaos.
3. Regulation and talent shortage add friction. According to Bitkom's 2026 report, the biggest AI obstacles in German companies are legal uncertainty (53%) and lack of expertise (53%). Even when a company recognizes its data silos, it often lacks the specialists to break them down — and the legal clarity to push AI projects forward.
What Doesn't Work: The Traditional Approach
The standard answer to data silos has been the same for years: ETL projects. Extract, Transform, Load. Push all data into a central data warehouse and analyze it there.
It sounds logical. In practice, it looks like this:
- Months before the first result. ETL projects in mid-market companies typically take 6 to 18 months before they're productive.
- High costs. External consultants, infrastructure, ongoing maintenance. Easily six figures before a single question gets answered.
- Rigid structures. Once built, the data architecture is hard to change. New sales channel? New marketing tool? Weeks of work again.
- Dependency on specialists. Every change requires a data engineer. The business department waits.
And even when the data warehouse is up and running, the business team still needs someone who writes SQL to extract answers. The controller asks the data analyst, who asks the data engineer, who asks the IT admin. By the time the answer arrives, the question has changed.
The real problem isn't the absence of data. The data is there — in every single system. The problem is that no one can consolidate and query it without turning it into a project.
The Alternative: Ready-Made Connectors Instead of Months of Integration
oneAgent tackles the silo problem from the other direction. Instead of copying and restructuring all data first, oneAgent connects directly to your existing systems — through ready-made connectors.
550+ connectors are available out of the box. Including:
- E-commerce: Shopware, Shopify, WooCommerce, Magento
- ERP: SAP, Microsoft Dynamics, DATEV
- CRM: Salesforce, HubSpot, Pipedrive
- Marketing: Google Ads, Meta Ads, LinkedIn Ads, Mailchimp
- Marketplaces: Amazon Seller Central, eBay, Otto
- Databases: PostgreSQL, MySQL, BigQuery, Snowflake
- Files: Excel, CSV, Google Sheets
The connection is established in minutes, not months. No ETL project. No data warehouse. No dependency on data engineers.
What Happens When the Silos Fall: A Concrete Example
Imagine an e-commerce company connecting five data sources with oneAgent: Shopware (shop), SAP (ERP), Google Ads (marketing), Meta Ads (marketing), and a returns management system.
Before: five isolated systems. Five different dashboards. Five different versions of the truth.
After: one platform where you ask questions like:
- "Which channel has the best margin after returns?"
- "How has the ROAS of Google Ads developed compared to Meta?"
- "Which products have high ad spend but low repeat purchase rate?"
- "How does customer lifetime value differ between direct shop customers and Amazon customers?"
- "Which product category is growing fastest — and is inventory aligned?"
These questions aren't exotic. They're the questions every management team asks. But without consolidated data, each one requires a custom report that has to be built manually — if the data can even be combined.
With oneAgent, you type the question and get the answer in seconds. In plain language. No SQL. With an automatic verification layer that ensures the answer is correct.
Why Asking Questions Beats Building Dashboards
A traditional BI dashboard shows you what someone thought was worth showing when the dashboard was built. It answers predefined questions. But business is unpredictable.
The most important question is often the one nobody anticipated: "Why is the return rate in category X 40% higher for the past two weeks?" No dashboard in the world has a pre-built chart for that.
oneAgent answers ad-hoc questions. Exactly the kind of questions that arise when you look at the numbers in the morning and something doesn't add up. Or when a sales manager asks why a specific customer is ordering less. Or when the executive team wants to know whether a new sales channel is actually profitable.
Three Steps Instead of Three Months
-
Choose your connectors. Which systems do you use? Shopware, SAP, Google Ads — select from over 550 ready-made connectors.
-
Establish the connection. Enter credentials, test the connection. In most cases, you're connected in under 30 minutes.
-
Start asking questions. From that moment on, you can query your data in natural language. The AI understands the relationships between your systems and delivers answers with source references.
No database knowledge required. No data warehouse. No ETL project that takes six months to deliver results.
Security and Compliance
Your data stays where it is. oneAgent reads data directly from your source systems — there is no central copy that could become an attack target. Processing is GDPR-compliant on servers in Frankfurt, Germany.
For companies with special security requirements, on-premise installation is also available. In that case, your data never leaves your own network at any point.
Conclusion: Data Silos Are Not Inevitable
76% of IT leaders recognize the problem. 89% of B2B companies struggle with data quality in AI projects. The numbers are clear: data silos are the biggest obstacle on the path to data-driven decisions.
But breaking down data silos doesn't have to mean months-long ETL projects, six-figure investments, and dependency on data specialists.
With 550+ ready-made connectors, oneAgent connects your existing systems in minutes. Without copying data. Without a data warehouse. Without SQL.
Instead: ask questions and get answers. From all your data sources. In one platform.
Try oneAgent free for 14 days — connect your first data sources and ask the questions you've been waiting weeks for. No credit card required.